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Tensorflow.data.Dataset.rejection_resample modifies my dataset's element_spec

I am trying to use tf.data.Dataset.rejection_resample to balance my dataset, but I am running into an issue in which the method modifies the element_spec of my dataset, making it incompatible with my models.
The original element spec of my dataset is:
({'input_A': TensorSpec(shape=(None, 900, 1), dtype=tf.float64, name=None),
'input_B': TensorSpec(shape=(None, 900, 1), dtype=tf.float64, name=None)},
TensorSpec(shape=(None, 1, 1), dtype=tf.int64, name=None))
This is the element spec after batching.
However, if I run rejection_resample (before batching), the element spec at the end becomes:
(TensorSpec(shape=(None,), dtype=tf.int64, name=None),
({'input_A': TensorSpec(shape=(None, 900, 1), dtype=tf.float64, name=None),
'input_B': TensorSpec(shape=(None, 900, 1), dtype=tf.float64, name=None)},
TensorSpec(shape=(None, 1, 1), dtype=tf.int64, name=None)))
So rejection_resample is adding another tf.int64 tensor in the beginning of my data, which I can't find out what is it for. My problem is that this breaks compatibility between the input data and my model, since it depends on the original input tuple.
Furthermore, it also causes an inconsistency between the training and validation data. I was expecting to apply rejection_resample only on training data, but if I do that, the training dataset will have the added tensor, while the validation one won't.
So my question is what is this added tensor to the element spec, and if there is any way to drop an element from the dataset after building it. Thank you.

Let suppose I have created the same dataset as yours,
x = tf.random.normal((7000, 900,1))
y = tf.random.normal((7000, 900,1))
z = tf.random.uniform((7000, 1,1), 1, 2, dtype=tf.int32)
#Now converting it to Tf.Dataset object
dataset = tf.data.Dataset.from_tensor_slices(((x,y),z))
func = lambda x , y : (({'input_A' : x[0], 'input_B' : x[1]}), y)
dataset = dataset.map(func)
After mapping my dataset will look exactly like yours
<MapDataset element_spec=({'input_A': TensorSpec(shape=(900, 1), dtype=tf.float32, name=None), 'input_B': TensorSpec(shape=(900, 1), dtype=tf.float32, name=None)}, TensorSpec(shape=(1, 1), dtype=tf.int32, name=None))>
Now, I have to remove this last Tensor
disjoint_func = lambda x , y :(x)
dataset = dataset.map(disjoint_func)
Now, the extra Tensor has been removed
<MapDataset element_spec={'input_A': TensorSpec(shape=(900, 1), dtype=tf.float32, name=None), 'input_B': TensorSpec(shape=(900, 1), dtype=tf.float32, name=None)}>

I can't tell you where the added tensor comes from but here would be an example how to remove/drop it from your dataset:
import tensorflow as tf
import numpy as np
# creating a sample dataset that's similar to your 'wrong' output
ds = tf.data.Dataset.from_tensor_slices((np.arange(-10, 0),(tf.constant(np.arange(10)), tf.constant(np.arange(10,20)))))
# remove the new 'wrong' tensor
dds = ds.map(lambda x, y: y)
# check new dataset
for i in dds.take(2):
print(i)
Keep in mind that this is a workaround and doesn't remove the source that causes the additional tensor

ValueError: TensorFlow2 Input 0 is incompatible with layer model

I am trying to code a ResNet CNN architecture based on the paper by using Python3, TensorFlow2 and CIFAR-10 dataset. You can access the Jupyter notebook here.
During training the model using "model.fit()", after just one epoch of training, I get the following error:
ValueError: Input 0 is incompatible with layer model: expected
shape=(None, 32, 32, 3), found shape=(32, 32, 3)
The training images are batched using batch_size = 128, hence the training loop gives the following 4-d tensor which TF Conv2D expects- (128, 32, 32, 3).
What's the source of this error?

Ok, I found a small issue in your code. The problem occurs in the test data set. You forget to transform it properly. So currently you have like this
images, labels = next(iter(test_dataset))
images.shape, labels.shape
(TensorShape([32, 32, 3]), TensorShape([10]))
You need to do the same transformation on the test as you did on the train set. But of course, things you consider: no shuffling, no augmentation.
def testaugmentation(x, y):
x = tf.image.resize_with_crop_or_pad(x, HEIGHT + 8, WIDTH + 8)
x = tf.image.random_crop(x, [HEIGHT, WIDTH, NUM_CHANNELS])
return x, y
def normalize(x, y):
x = tf.image.per_image_standardization(x)
return x, y
test_dataset = (test_dataset
.map(testaugmentation)
.map(normalize)
.batch(batch_size = batch_size, drop_remainder = True))
images, labels = next(iter(test_dataset))
images.shape, labels.shape
(TensorShape([128, 32, 32, 3]), TensorShape([128, 10]))

tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot convert a Tensor of dtype resource to a numpy array

I am trying to design a GAN using tensorflow.keras models and layers classes. I made a discriminator that takes in a list of 2 pictures and outputs a Dense sigmoid activated percentage of similarity:
prediction = Dense(1, activation = "sigmoid")(Flatten()(conv4))
model = Model(inputs = [firstImage, secondImage], outputs = prediction)
Then a generator that takes in a random one dimension vector and returns a picture out of it:
generated = Conv2D(3, kernel_size = (4, 4), padding = "same",
kernel_initializer = kernelInit, activation = "sigmoid")(conv5) # output shape (256, 256, 3)
model = Model(inputs = noise, outputs = generated)
I made a custom generator using a keras.ImageDataGenerator.flow_from_directory() to load in pictures:
def loadRealImages(batch):
for gen in pixGen.flow_from_directory(picturesPath, target_size = (256, 256),
batch_size = batch, class_mode = "binary"):`
yield gen
I didn't have any trouble compiling any of these two but then when I try to link them together into an adversarial model with this code:
inNoise = Input(shape = (generatorInNoise,))
fake = generator(inNoise) # get one fake
real = np.array(next(loadRealImages(1))[0], dtype = np.float32) # get one real image
discriminator.trainable = False # lock discriminator weights
prediction = discriminator([real, fake]) # check similarity
adversarial = Model(inputs = inNoise, outputs = [fake, prediction]) # set adversarial model
I get this error on the last line:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array.
I ascertained the shape of inNoise, fake and prediction:
<class 'tensorflow.python.framework.ops.Tensor'> (None, 16) Tensor("input_4:0", shape=(None, 16), dtype=float32)
<class 'tensorflow.python.framework.ops.Tensor'> (None, 256, 256, 3) Tensor("model_1/Identity:0", shape=(None, 256, 256, 3), dtype=float32)
<class 'tensorflow.python.framework.ops.Tensor'> (1, 1) Tensor("dense_2/Identity:0", shape=(1, 1), dtype=float32)
But I still can't figure out what is raising the error and looking it up on google didn't really give me any pointers either. Can anyone help with this?

At the core, the issue here is that you're trying to make a numpy array a part of the computation graph. This can lead to undefined behaviour depending on how you use it. Some minor changes to you code can help:
inNoise = Input(shape = (generatorInNoise,))
fake = generator(inNoise) # get one fake
real = Input((real_image_shape)) # get one real image
discriminator.trainable = False # lock discriminator weights
prediction = discriminator([real, fake]) # check similarity
adversarial = Model(inputs = [inNoise, real], outputs = [fake, prediction]) # set adversarial model
As you can see, the real image needs to be provided as an input to the model, not derived as a part of it.

NeuPy: Input shapes issues

I want to build a neural network using neupy.
Therefore I consturcted the following architecture:
network = layers.join(
layers.Input(10),
layers.Linear(500),
layers.Relu(),
layers.Linear(300),
layers.Relu(),
layers.Linear(10),
layers.Softmax(),
)
My data is shaped as follwoing:
x_train.shape = (32589,10)
y_train.shape = (32589,1)
When I try to train this network using:
model.train(x_train, y_trian)
I get the follwoing error:
ValueError: Input dimension mis-match. (input[0].shape[1] = 10, input[1].shape[1] = 1)
Apply node that caused the error: Elemwise{sub,no_inplace}(SoftmaxWithBias.0, algo:network/var:network-output)
Toposort index: 26
Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)]
Inputs shapes: [(32589, 10), (32589, 1)]
Inputs strides: [(80, 8), (8, 8)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[Elemwise{Composite{((i0 * i1) / i2)}}(TensorConstant{(1, 1) of 2.0}, Elemwise{sub,no_inplace}.0, Elemwise{mul,no_inplace}.0), Elemwise{Sqr}[(0, 0)](Elemwise{sub,no_inplace}.0)]]
How do I have to edit my network to map this kind of data?
Thank you a lot!

Your architecture has 10 outputs instead of 1. I assume that your y_train function is a 0-1 class identifier. If so, than you need to change your structure to this:
network = layers.join(
layers.Input(10),
layers.Linear(500),
layers.Relu(),
layers.Linear(300),
layers.Relu(),
layers.Linear(1), # Single output
layers.Sigmoid(), # Sigmoid works better for 2-class classification
)
You can make it even simpler
network = layers.join(
layers.Input(10),
layers.Relu(500),
layers.Relu(300),
layers.Sigmoid(1),
)
The reason why it works is because layers.Liner(10) > layers.Relu() is the same as layers.Relu(10). You can learn more in official documentation: http://neupy.com/docs/layers/basics.html#mutlilayer-perceptron-mlp

keras / tensorflow requires unnecessary values fed to placeholders

I'm using Keras with TF backend. Recently, when using the functional API to make "hybrid" models, it seemed to me that Keras requires me to feed values that it shouldn't need.
As a background, I am trying to implement a conditional GAN in Keras. My implementation has a generator and a discriminator. As an example, the generator accepts (20, 20, 1) inputs and returns (20, 20, 1) outputs. These are stacked by channel to produce a (20, 20, 2) input to the discriminator. The discriminator is supposed to decide whether it is seeing a ground-truth translation of the original (20, 20, 1) image or a translation by the generator. This is represented by 0=fake, 1=real.
By itself, the discriminator is just a CNN for binary classification. Therefore, it can be trained by feeding data points with inputs of shape (20, 20, 2) and outputs in {0,1}. Therefore, if I write something like:
# <disc> is the discriminator
arbitrary_input = np.full(shape=(5, 20, 20, 2), fill_value=0.5)
arbitrary_labels = np.array([1, 1, 0, 0, 1])
disc.fit(arbitrary_input, arbitrary_labels, epochs=5)
training will proceed without errors (obviously this is a useless dataset, though).
However, when I insert the discriminator into the generator-discriminator stack:
# <disc> is the discriminator, <gen> is the generator
input = Input(shape=(20, 20, 1), name='stack_input')
gen_output = gen(input)
pair = Concatenate(axis=FEATURES_AXIS)([input, gen_output])
disc_output = disc(gen_output)
stack = Model(input, disc_output)
stack.compile(optimizer='adam', loss='binary_crossentropy')
arbitrary_input = np.full(shape=(5, 20, 20, 2), fill_value=0.5)
arbitrary_labels = np.array([1, 1, 0, 0, 1])
disc.fit(arbitrary_input, arbitrary_labels, epochs=5)
suddenly I need to feed an extra placeholder. I get this error message on disc.fit():
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'stack_input' with dtype float
[[Node: stack_input = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
As you can see by the name, this is the input to the hybrid/stacked model. I haven't changed the discriminator at all, I have only included it in another model. Therefore disc.fit() should still work, right?
There's a workaround available by freezing the weights of the generator and using fit() on the full stack, I think, but I do not understand why the method above doesn't work.
Is it perhaps some issue with scoping?
Edit: The discriminator is really just a simple CNN. It is initialized with disc = pix2pix_discriminator(input_shape=(20, 20, 2), n_filters=(32, 64)). The function in question is:
def pix2pix_discriminator(input_shape, n_filters, kernel_size=4, strides=2, padding='same', alpha=0.2):
x = Input(shape=input_shape, name='disc_input')
# first layer
h = Conv2D(filters=n_filters[0],
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=DATA_FORMAT)(x)
# no BatchNorm
h = LeakyReLU(alpha=alpha)(h)
for i in range(1, len(n_filters)):
h = Conv2D(filters=n_filters[i],
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=DATA_FORMAT)(h)
h = BatchNorm(axis=FEATURES_AXIS)(h)
h = LeakyReLU(alpha=alpha)(h)
h_flatten = Flatten()(h) # required for the upcoming Dense layer
y_pred = Dense(units=1, activation='sigmoid')(h_flatten) # binary output
discriminator = Model(inputs=x, outputs=y_pred)
discriminator.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return discriminator